#include <ocr_rec.h>
namespace PaddleOCR {

    void CRNNRecognizer::Run(cv::Mat& img, std::vector<double>* times,
                             std::string& rec_strs,double& rec_scores) {
        cv::Mat srcimg;
        img.copyTo(srcimg);
        cv::Mat resize_img;

        float wh_ratio = float(srcimg.cols) / float(srcimg.rows);
        auto preprocess_start = std::chrono::steady_clock::now();
        this->resize_op_.Run(srcimg, resize_img, wh_ratio, this->use_tensorrt_);

        this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
            this->is_scale_);

        std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);

        this->permute_op_.Run(&resize_img, input.data());
        auto preprocess_end = std::chrono::steady_clock::now();

        // Inference.
        auto input_names = this->predictor_->GetInputNames();
        auto input_t = this->predictor_->GetInputHandle(input_names[0]);
        input_t->Reshape({ 1, 3, resize_img.rows, resize_img.cols });
        auto inference_start = std::chrono::steady_clock::now();
        input_t->CopyFromCpu(input.data());
        this->predictor_->Run();

        std::vector<float> predict_batch;
        auto output_names = this->predictor_->GetOutputNames();
        auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
        auto predict_shape = output_t->shape();

        int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
            std::multiplies<int>());
        predict_batch.resize(out_num);

        output_t->CopyToCpu(predict_batch.data());
        auto inference_end = std::chrono::steady_clock::now();

        // ctc decode
        auto postprocess_start = std::chrono::steady_clock::now();
        std::vector<std::string> str_res;
        std::string res  ="";
        int argmax_idx;
        int last_index = 0;
        float score = 0.f;
        int count = 0;
        float max_value = 0.0f;

        for (int n = 0; n < predict_shape[1]; n++) {
            argmax_idx =
                int(Utility::argmax(&predict_batch[n * predict_shape[2]],
                    &predict_batch[(n + 1) * predict_shape[2]]));
            max_value =
                float(*std::max_element(&predict_batch[n * predict_shape[2]],
                    &predict_batch[(n + 1) * predict_shape[2]]));

            if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
                score += max_value;
                count += 1;
                str_res.push_back(label_list_[argmax_idx]);
            }
            last_index = argmax_idx;
        }
        auto postprocess_end = std::chrono::steady_clock::now();
        score /= count;
        for (int i = 0; i < str_res.size(); i++) {
            res += std::string(str_res[i]);
        }
        // 赋值
        rec_strs = res;
        rec_scores = score;

        std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
        times->push_back(double(preprocess_diff.count() * 1000));
        std::chrono::duration<float> inference_diff = inference_end - inference_start;
        times->push_back(double(inference_diff.count() * 1000));
        std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
        times->push_back(double(postprocess_diff.count() * 1000));
    }

    void CRNNRecognizer::LoadModel(const std::string& model_dir) {
        //   AnalysisConfig config;
        paddle_infer::Config config;
        config.SetModel(model_dir + "/inference.pdmodel",
            model_dir + "/inference.pdiparams");

        if (this->use_gpu_) {
            config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
            if (this->use_tensorrt_) {
                auto precision = paddle_infer::Config::Precision::kFloat32;
                if (this->precision_ == "fp16") {
                    precision = paddle_infer::Config::Precision::kHalf;
                }
                if (this->precision_ == "int8") {
                    precision = paddle_infer::Config::Precision::kInt8;
                }
                config.EnableTensorRtEngine(
                    1 << 20, 10, 3,
                    precision,
                    false, false);
                std::map<std::string, std::vector<int>> min_input_shape = {
                    {"x", {1, 3, 32, 10}} };
                std::map<std::string, std::vector<int>> max_input_shape = {
                    {"x", {1, 3, 32, 2000}} };
                std::map<std::string, std::vector<int>> opt_input_shape = {
                    {"x", {1, 3, 32, 320}} };

                config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
                    opt_input_shape);
            }
        }
        else {
            config.DisableGpu();
            if (this->use_mkldnn_) {
                config.EnableMKLDNN();
                // cache 10 different shapes for mkldnn to avoid memory leak
                config.SetMkldnnCacheCapacity(10);
            }
            config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
        }

        config.SwitchUseFeedFetchOps(false);
        // true for multiple input
        config.SwitchSpecifyInputNames(true);

        config.SwitchIrOptim(true);

        config.EnableMemoryOptim();
        config.DisableGlogInfo();

        this->predictor_ = CreatePredictor(config);
    }

} // namespace PaddleOCR
